About the role
The Data & Analytics Platforms Team (DAP) develops and runs central platforms and data products, which enable the Allianz Operating Entities (OEs) to generate value from data & analytics at scale. The major offering is the Allianz Data Platform (ADP), a PaaS offering that comprises the necessary capabilities to store, transfer and analyse data, as well as running data & analytics products.
Are you energised by building and implementing the data platforms that thousands of colleagues depend on at one of the world's largest financial services groups?If so, this role puts you at the centre of that work. You will implement and engineer the platforms behind the Allianz Data Platform, working hands-on with Databricks, Azure Synapse, and Microsoft Fabric, and turning platform capabilities into services that Operating Entities can adopt with confidence. The role sits at the crossroads of engineering, data, and operations, which means the pipelines, integrations, and controls you build shape how thousands of colleagues across Allianz Group turn data into decisions. To get there, you will work forward with the Operating Entities themselves, assisting in the deployment and configuration of platform components, integration of source systems, and extending our offering into so that analytics and AI use cases run on solid, governed foundations.
What you doConsult with OEs in the implementation and configuration of core platform components across Databricks, Azure Synapse, and Microsoft Fabric, from initial deployment through to stable production.
Assist in building data integration pipelines that connect OE source systems to central platforms, using both batch and streaming patterns.
Configure platform capabilities with high availability, strong access controls, and end-to-end observability.




Extend the platform offering into AI and GenAI scenarios, including Azure AI Foundry, so that model-based use cases run on governed foundations.
Partner directly with Operating Entities to translate business needs into working platform implementations and reusable solutions.
Implement governance, security, and data control layers in line with financial services expectations for data handling, auditability, and operational resilience.
Build and refine CI/CD and infrastructure-as-code pipelines to keep deployment cycles fast, secure, and repeatable.
Collaborate closely with security, risk, compliance, architecture, and data governance teams to clear the path for platform adoption.
Deliver reusable components, reference architectures, and engineering best practices that accelerate platform rollout across the group.
Support the rollout of central platform solutions to the OEs, and build business-specific use cases on top of them.
Coach and mentor engineers across the organisation, and drive continuous improvement in tooling, standards, and ways of working.
What you bringEssential skills and

experience:
BS/MS in Computer Science, Information Systems, Engineering, or an equivalent technical discipline.5+ years of hands-on experience implementing or engineering data and analytics platforms in enterprise environments, ideally within financial services, insurance, or another regulated industry.
Significant experience with Databricks, including Delta Lake, Unity Catalogue,



workflows, and production pipelines.
Practical experience with Azure data services such as Azure Synapse Analytics and Microsoft Fabric.
Strong data integration skills, covering ETL and ELT design, orchestration (e.
G., Azure Data Factory, Airflow), and both batch and streaming sources.
Solid engineering skills in Python and SQL, with experience building and maintaining production-grade code.
Experience with CI/CD pipelines, source control management, and infrastructure-as-code (e.
G., Terraform, Bicep).
Working knowledge of platform security and governance concepts, including access controls, encryption, and data lineage.
Familiarity with end-to-end (E2E) Data Product Management, including data governance, quality, and lifecycle management.
Excellent communication and stakeholder management skills in English (additional languages are a plus).
It would be great if you also have (not required):7+ years of experience in platform engineering, data engineering, or cloud infrastructure.
Exposure to Azure AI Foundry or comparable AI platform tooling, including the deployment of model-based services.
Familiarity with MLOps practices and tools such as MLflow, feature stores, and model lifecycle governance.
Experience with Kubernetes, Docker, and cloud-native architectures.
Hands-on familiarity with the wider modern data stack (e.
G., Spark, dbt, Kafka, Snowflake, or similar).
Experience with observability and monitoring tools such as Azure Monitor, Datadog, or Prometheus.
Relevant certifications such as Databricks Certified Data Engineer, Microsoft Azure Data Engineer, or equivalent.
If you don’t tick every box, that’s okay—your growth mindset matters. We welcome applicants who bring the essentials an

📌 Forward Deployed Engineer (Barcelona)
🏢 Allianz Technology
📍 Barcelona